Pharmacovigilance: HTTP Interface and External Systems

Data Characteristics

Pharmacovigilance data originates from clinical trial reports, real-world studies, adverse event reporting systems (e.g., international drug regulatory databases), and medical literature. This data exists in both structured and unstructured formats. Structured data includes patient demographics, drug information, adverse reaction descriptions, severity, and outcomes, typically in CSV, JSON, or XML. Unstructured data encompasses free-text patient medical records, physician diagnoses, and adverse reaction narratives. Data update frequency varies from daily to monthly, depending on reporting mechanisms and regulatory requirements. Field units often include milligrams (mg), grams (g), or international units (IU) for dosage, and days, weeks, months, or years for time. Document structures are complex and may involve multiple nested layers or links to external entities.

Constraints Imposed by These Characteristics on HTTP Interfaces and External Systems

The diversity and complexity of pharmacovigilance data demand robust HTTP interface design. Structured data requires precise field mapping and data type validation to ensure data integrity and consistency. Importing unstructured text necessitates pre-processing with natural language processing techniques for subsequent information extraction and analysis. The uncertain data update frequency requires interfaces to support incremental or periodic full synchronization and include version control for data changes and traceability. Multi-layered document structures in API responses must balance readability and extensibility. Handling sensitive patient information requires strict adherence to data security and privacy standards during data transmission and storage, such as HTTPS encryption and access control mechanisms.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_REQUEST_TIMEOUT`	`60 seconds`	Prevents premature timeouts, considering potentially large data volumes and external system latency.
`MAX_BODY_SIZE`	`50 MB`	Accommodates batch adverse event reports that may contain long text or multiple records.
`CONCURRENT_REQUESTS`	`Based on actual measurements`	Determine after stress testing against external system load capacity and FastGPT instance resources.
`RETRY_ATTEMPTS`	`3 times`	Addresses network fluctuations or transient external system failures, improving data transmission success rates.
`KNOWLEDGE_BASE_ID`	`Specific knowledge base ID`	Ensures the workflow accurately calls the relevant pharmacovigilance knowledge base for information retrieval and analysis.
`FIELD_MAPPING_RULES`	`JSON configuration`	Provides precise mapping based on external system field names and internal data models, handling data heterogeneity.

Common Pitfalls

An API call returns 400 Bad Request because the external system received a JSON structure that did not match expectations, possibly due to a missing mandatory field or a data type mismatch.
Key fields are empty in the received data, leading to information loss. This typically results from incorrect HTTP interface field mapping configurations that fail to correctly parse complex external system data structures.
Workflow execution times out, failing to process a large influx of adverse event reports in time. This often occurs when HTTP_REQUEST_TIMEOUT is set too short or concurrent request limits are not optimized for actual load.

Verification Steps

Perform simulated calls to the HTTP interface using representative pharmacovigilance data samples. Verify the completeness and accuracy of the response, especially for key field values.
Trigger a workflow within FastGPT. Monitor the logs for interactions with the external system, confirming correct request parameters, response status codes, and data parsing.
Design end-to-end test cases covering common adverse reaction reporting scenarios. Validate the entire process from data import from the external system to the knowledge base, and its accurate retrieval and citation by the AI Agent.

The values provided are common starting points and should be measured against specific samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.